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import sys
from pathlib import Path
import hydra
import torch
project_root = Path(__file__).resolve().parent.parent
sys.path.append(str(project_root))
from yolo.config.config import Config
from yolo.model.yolo import create_model
from yolo.tools.data_loader import create_dataloader
from yolo.tools.solver import ModelTester, ModelTrainer
from yolo.utils.deploy_utils import FastModelLoader
from yolo.utils.logging_utils import custom_logger, validate_log_directory
@hydra.main(config_path="config", config_name="config", version_base=None)
def main(cfg: Config):
custom_logger()
save_path = validate_log_directory(cfg, exp_name=cfg.name)
dataloader = create_dataloader(cfg.task.data, cfg.dataset, cfg.task.task)
device = torch.device(cfg.device)
if getattr(cfg.task, "fast_inference", False):
model = FastModelLoader(cfg).load_model()
device = torch.device(cfg.device)
else:
model = create_model(cfg).to(device)
if cfg.task.task == "train":
trainer = ModelTrainer(cfg, model, save_path, device)
trainer.solve(dataloader)
if cfg.task.task == "inference":
tester = ModelTester(cfg, model, save_path, device)
tester.solve(dataloader)
if __name__ == "__main__":
main()
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